{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "In this notebook we will demonstrates how to apply post-training quantization on a keras model with BigDL-Nano.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Environment\n",
    "Before you start with Apis delivered by bigdl-nano, you have to make sure BigDL-Nano is correctly installed for TensorFlow. If not, please follow [this](../../../../../docs/readthedocs/source/doc/Nano/Overview/nano.md) to set up your environment.<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "By default, [Intel Neural Compressor](https://github.com/intel/neural-compressor) is not installed with BigDL-Nano. So if you determine to use it as your quantization backend, you'll need to install it first:\n",
    "```bash\n",
    "pip install neural-compressor==1.11.0\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BigDL-Nano provides several APIs which can help users easily apply optimizations on inference pipelines to improve latency and throughput. The Keras InferenceOptimizer(`bigdl.nano.tf.keras.InferenceOptimizer`) provides the APIs for all optimizations you need for inference.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bigdl.nano.tf.keras import Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data\n",
    "Here we load data from tensorflow_datasets. The [Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from the Imagenet dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "DATANAME = 'imagenette/320px-v2'\n",
    "(train_ds, test_ds), info = tfds.load(DATANAME, data_dir='../data/',\n",
    "                                     split=['train', 'validation'],\n",
    "                                     with_info=True,\n",
    "                                     as_supervised=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the Data\n",
    "The following code shows the first 6 images with their labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >image</th>        <th class=\"col_heading level0 col1\" >label</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row0_col0\" class=\"data row0 col0\" ><img src=\"\" alt=\"Img\" /></td>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row0_col1\" class=\"data row0 col1\" >2 (n02979186)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row1_col0\" class=\"data row1 col0\" ><img src=\"\" alt=\"Img\" /></td>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row1_col1\" class=\"data row1 col1\" >6 (n03417042)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row2_col0\" class=\"data row2 col0\" ><img src=\"\" alt=\"Img\" /></td>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row2_col1\" class=\"data row2 col1\" >7 (n03425413)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row3_col0\" class=\"data row3 col0\" ><img src=\"\" alt=\"Img\" /></td>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row3_col1\" class=\"data row3 col1\" >3 (n03000684)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row4_col0\" class=\"data row4 col0\" ><img src=\"\" alt=\"Img\" /></td>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row4_col1\" class=\"data row4 col1\" >4 (n03028079)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row5_col0\" class=\"data row5 col0\" ><img src=\"\" alt=\"Img\" /></td>\n",
       "                        <td id=\"T_3707d62e_0ba4_11ed_826c_0242ac110002row5_col1\" class=\"data row5 col1\" >5 (n03394916)</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "                                               image  label\n",
       "0  [[[254, 254, 254], [254, 254, 254], [254, 254,...      2\n",
       "1  [[[44, 39, 43], [132, 127, 131], [219, 214, 21...      6\n",
       "2  [[[0, 10, 11], [0, 11, 12], [0, 11, 12], [0, 1...      7\n",
       "3  [[[179, 169, 118], [173, 167, 115], [172, 171,...      3\n",
       "4  [[[9, 14, 17], [0, 4, 7], [8, 13, 16], [34, 39...      4\n",
       "5  [[[62, 51, 29], [63, 52, 30], [65, 55, 30], [6...      5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfds.as_dataframe(train_ds.take(6), info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare Inputs\n",
    "Here we resize the input image to uniform `IMG_SIZE` and the labels are put into one_hot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Dataset that includes only 1/num_shards of full dataset.\n",
    "from operator import index\n",
    "import os\n",
    "num_shards = int(os.environ.get('NUM_SHARDS', 1))\n",
    "train_ds = train_ds.shard(num_shards, index=0)\n",
    "test_ds = test_ds.shard(num_shards, index=0)\n",
    "\n",
    "import tensorflow as tf\n",
    "img_size = 224\n",
    "num_classes = info.features['label'].num_classes\n",
    "\n",
    "# we need an unbatched dataset to do quantization\n",
    "calib_ds = test_ds.map(lambda img, label: (tf.image.resize(img, (img_size, img_size)), tf.one_hot(label, num_classes)))\n",
    "\n",
    "train_ds = train_ds.map(lambda img, label: (tf.image.resize(img, (img_size, img_size)), tf.one_hot(label, num_classes))).batch(32)\n",
    "test_ds = test_ds.map(lambda img, label: (tf.image.resize(img, (img_size, img_size)), tf.one_hot(label, num_classes))).batch(32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Build Model\n",
    "Here we initialize the ResNet50 from `tf.keras.applications` with pre-trained ImageNet weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras import layers, Model\n",
    "inputs = tf.keras.layers.Input(shape=(224, 224, 3))\n",
    "x = tf.cast(inputs, tf.float32)\n",
    "x = tf.keras.applications.resnet50.preprocess_input(x)\n",
    "backbone = ResNet50(weights='imagenet')\n",
    "backbone.trainable = False\n",
    "x = backbone(x)\n",
    "x = layers.Dense(512, activation='relu')(x)\n",
    "outputs = layers.Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "296/296 [==============================] - 52s 168ms/step - loss: 0.5111 - accuracy: 0.9658\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f264c166210>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_ds, epochs=1)\n",
    "\n",
    "# testing\n",
    "from time import time\n",
    "start = time()\n",
    "his = model.evaluate(test_ds)\n",
    "fp32_infer_time = time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantization with Intel Neural Compressor\n",
    "[`Model.quantize()`](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/tensorflow.html#bigdl.nano.tf.keras.Model) return a Keras module with desired precision and accuracy. Taking Resnet50 as an example, you can add quantization as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-24 23:13:08 [WARNING] Override the value of `metric` field defined in yaml file as user defines the value of `metric` attribute by code.\n",
      "2022-07-24 23:13:09.188297: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
      "2022-07-24 23:13:09.188479: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\n",
      "2022-07-24 23:13:09.204934: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1149] Optimization results for grappler item: graph_to_optimize\n",
      "  function_optimizer: function_optimizer did nothing. time = 0.006ms.\n",
      "  function_optimizer: function_optimizer did nothing. time = 0.001ms.\n",
      "\n",
      "2022-07-24 23:13:10.769523: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
      "2022-07-24 23:13:10.769664: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\n",
      "2022-07-24 23:13:11.208401: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1149] Optimization results for grappler item: tf_graph\n",
      "  constant_folding: Graph size after: 573 nodes (-324), 912 edges (-324), time = 169.181ms.\n",
      "  constant_folding: Graph size after: 573 nodes (0), 912 edges (0), time = 137.145ms.\n",
      "\n",
      "2022-07-24 23:13:11 [INFO] ConvertLayoutOptimizer elapsed time: 0.84 ms\n",
      "2022-07-24 23:13:15.805196: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
      "2022-07-24 23:13:15.805337: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\n",
      "2022-07-24 23:13:16.164853: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1149] Optimization results for grappler item: graph_to_optimize\n",
      "  model_pruner: Graph size after: 521 nodes (-52), 808 edges (-104), time = 97.153ms.\n",
      "  shape_optimizer: shape_optimizer did nothing. time = 0.986ms.\n",
      "  dependency_optimizer: Graph size after: 468 nodes (-53), 483 edges (-325), time = 27.888ms.\n",
      "  debug_stripper: debug_stripper did nothing. time = 0.878ms.\n",
      "  loop_optimizer: Graph size after: 468 nodes (0), 483 edges (0), time = 24.229ms.\n",
      "  model_pruner: Graph size after: 468 nodes (0), 483 edges (0), time = 24.579ms.\n",
      "  shape_optimizer: shape_optimizer did nothing. time = 0.643ms.\n",
      "  dependency_optimizer: Graph size after: 468 nodes (0), 483 edges (0), time = 24.988ms.\n",
      "  debug_stripper: debug_stripper did nothing. time = 0.666ms.\n",
      "\n",
      "2022-07-24 23:13:16 [INFO] Pass GrapplerOptimizer elapsed time: 4320.33 ms\n",
      "2022-07-24 23:13:17 [INFO] Pass SwitchOptimizer elapsed time: 840.26 ms\n",
      "2022-07-24 23:13:17 [INFO] Pass RemoveTrainingNodesOptimizer elapsed time: 609.95 ms\n",
      "2022-07-24 23:13:17 [INFO] Pass SplitSharedInputOptimizer elapsed time: 4.33 ms\n",
      "2022-07-24 23:13:18 [INFO] Pass GraphFoldConstantOptimizer elapsed time: 612.86 ms\n",
      "2022-07-24 23:13:18 [INFO] Pass FuseColumnWiseMulOptimizer elapsed time: 603.47 ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/anaconda3/envs/nanoTensorflow/lib/python3.7/site-packages/neural_compressor/adaptor/tf_utils/util.py:322: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-24 23:13:19 [WARNING] From /root/anaconda3/envs/nanoTensorflow/lib/python3.7/site-packages/neural_compressor/adaptor/tf_utils/util.py:322: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
      "2022-07-24 23:13:21 [INFO] Pass StripUnusedNodesOptimizer elapsed time: 2428.79 ms\n",
      "2022-07-24 23:13:21 [INFO] Pass GraphCseOptimizer elapsed time: 611.77 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass FoldBatchNormNodesOptimizer elapsed time: 1571.7 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass UpdateEnterOptimizer elapsed time: 59.61 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass ConvertLeakyReluOptimizer elapsed time: 62.76 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass ConvertAddToBiasAddOptimizer elapsed time: 61.43 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass FuseTransposeReshapeOptimizer elapsed time: 64.46 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass FuseConvWithMathOptimizer elapsed time: 62.13 ms\n",
      "2022-07-24 23:13:23 [INFO] Pass ExpandDimsOptimizer elapsed time: 62.68 ms\n",
      "2022-07-24 23:13:24 [INFO] Pass InjectDummyBiasAddOptimizer elapsed time: 65.17 ms\n",
      "2022-07-24 23:13:24 [INFO] Pass MoveSqueezeAfterReluOptimizer elapsed time: 81.12 ms\n",
      "2022-07-24 23:13:24 [INFO] Pass Pre Optimization elapsed time: 13021.08 ms\n",
      "2022-07-24 23:13:25 [INFO] Get FP32 model baseline.\n",
      "2022-07-24 23:13:25 [INFO] Start to evaluate the TensorFlow model.\n",
      "2022-07-24 23:13:52 [INFO] Model inference elapsed time: 27376.86 ms\n",
      "2022-07-24 23:13:52 [INFO] Save tuning history to /home/projects/BigDL/python/nano/notebooks/tensorflow/tutorial/nc_workspace/2022-07-24_23-13-08/./history.snapshot.\n",
      "2022-07-24 23:13:52 [INFO] FP32 baseline is: [Accuracy: 0.9794, Duration (seconds): 27.3783]\n",
      "2022-07-24 23:13:52 [WARNING] Reset `calibration.dataloader.batch_size` field to 25 to make sure the sampling_size is divisible exactly by batch size\n",
      "2022-07-24 23:13:52 [WARNING] Found possible input node names: ['input_1'], output node names: ['dense_1'].\n",
      "2022-07-24 23:13:53 [WARNING] Found possible input node names: ['input_1'], output node names: ['dense_1'].\n",
      "2022-07-24 23:13:54.179591: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
      "2022-07-24 23:13:54.179743: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\n",
      "2022-07-24 23:13:54.196076: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1149] Optimization results for grappler item: graph_to_optimize\n",
      "  function_optimizer: function_optimizer did nothing. time = 0.005ms.\n",
      "  function_optimizer: function_optimizer did nothing. time = 0.002ms.\n",
      "\n",
      "2022-07-24 23:13:55.571069: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
      "2022-07-24 23:13:55.571209: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\n",
      "2022-07-24 23:13:56.022530: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1149] Optimization results for grappler item: tf_graph\n",
      "  constant_folding: Graph size after: 573 nodes (-324), 912 edges (-324), time = 167.757ms.\n",
      "  constant_folding: Graph size after: 573 nodes (0), 912 edges (0), time = 134.91ms.\n",
      "\n",
      "2022-07-24 23:14:04 [INFO] Pass Quantization elapsed time: 7860.73 ms\n",
      "2022-07-24 23:14:09 [INFO] Start sampling on calibration dataset.\n",
      ";model/resnet50/conv1_conv/Conv2D_eightbit_min_model/resnet50/conv1_pad/Pad__print__;__min:[-123.68]\n",
      ";model/resnet50/conv1_conv/Conv2D_eightbit_max_model/resnet50/conv1_pad/Pad__print__;__max:[151.061]\n",
      ";model/resnet50/conv1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[19.4159355]\n",
      ";model/resnet50/pool1_pool/MaxPool_eightbit_min_model/resnet50/pool1_pad/Pad__print__;__min:[0]\n",
      ";model/resnet50/pool1_pool/MaxPool_eightbit_max_model/resnet50/pool1_pad/Pad__print__;__max:[19.4159355]\n",
      ";model/resnet50/pool1_pool/MaxPool_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv2_block1_1_conv/Conv2D_eightbit_min_model/resnet50/pool1_pool/MaxPool__print__;__min:[0]\n",
      ";model/resnet50/conv2_block1_0_conv/Conv2D_eightbit_min_model/resnet50/pool1_pool/MaxPool__print__;__min:[0]\n",
      ";model/resnet50/pool1_pool/MaxPool_eightbit_requant_range__print__;__requant_max:[19.4159355]\n",
      ";model/resnet50/conv2_block1_1_conv/Conv2D_eightbit_max_model/resnet50/pool1_pool/MaxPool__print__;__max:[19.4159355]\n",
      ";model/resnet50/conv2_block1_0_conv/Conv2D_eightbit_max_model/resnet50/pool1_pool/MaxPool__print__;__max:[19.4159355]\n",
      ";model/resnet50/conv2_block1_2_conv/Conv2D_eightbit_min_model/resnet50/conv2_block1_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv2_block1_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv2_block1_2_conv/Conv2D_eightbit_max_model/resnet50/conv2_block1_1_relu/Relu__print__;__max:[8.98138905]\n",
      ";model/resnet50/conv2_block1_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[8.98138905]\n",
      ";model/resnet50/conv2_block1_3_conv/Conv2D_eightbit_min_model/resnet50/conv2_block1_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv2_block1_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv2_block1_3_conv/Conv2D_eightbit_max_model/resnet50/conv2_block1_2_relu/Relu__print__;__max:[12.5747442]\n",
      ";model/resnet50/conv2_block1_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[12.5747442]\n",
      ";model/resnet50/conv2_block1_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[-12.1864529]\n",
      ";model/resnet50/conv2_block1_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[11.0445147]\n",
      ";model/resnet50/conv2_block2_1_conv/Conv2D_eightbit_min_model/resnet50/conv2_block1_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv2_block1_0_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv2_block2_1_conv/Conv2D_eightbit_max_model/resnet50/conv2_block1_out/Relu__print__;__max:[18.3545055]\n",
      ";model/resnet50/conv2_block1_0_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[18.3545055]\n",
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      ";model/resnet50/conv4_block1_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block1_2_conv/Conv2D_eightbit_max_model/resnet50/conv4_block1_1_relu/Relu__print__;__max:[17.2523308]\n",
      ";model/resnet50/conv4_block1_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[17.2523308]\n",
      ";model/resnet50/conv4_block1_3_conv/Conv2D_eightbit_min_model/resnet50/conv4_block1_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block1_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block1_3_conv/Conv2D_eightbit_max_model/resnet50/conv4_block1_2_relu/Relu__print__;__max:[14.0734501]\n",
      ";model/resnet50/conv4_block1_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[14.0734501]\n",
      ";model/resnet50/conv4_block1_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[-16.1131573]\n",
      ";model/resnet50/conv4_block1_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[16.4721069]\n",
      ";model/resnet50/conv4_block2_1_conv/Conv2D_eightbit_min_model/resnet50/conv4_block1_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block1_0_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block2_1_conv/Conv2D_eightbit_max_model/resnet50/conv4_block1_out/Relu__print__;__max:[22.1721363]\n",
      ";model/resnet50/conv4_block1_0_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[22.1721363]\n",
      ";model/resnet50/conv4_block2_2_conv/Conv2D_eightbit_min_model/resnet50/conv4_block2_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block2_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block2_2_conv/Conv2D_eightbit_max_model/resnet50/conv4_block2_1_relu/Relu__print__;__max:[17.9055195]\n",
      ";model/resnet50/conv4_block2_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[17.9055195]\n",
      ";model/resnet50/conv4_block2_3_conv/Conv2D_eightbit_min_model/resnet50/conv4_block2_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block2_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block2_3_conv/Conv2D_eightbit_max_model/resnet50/conv4_block2_2_relu/Relu__print__;__max:[33.692]\n",
      ";model/resnet50/conv4_block2_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[33.692]\n",
      ";model/resnet50/conv4_block3_1_conv/Conv2D_eightbit_min_model/resnet50/conv4_block2_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block2_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block3_1_conv/Conv2D_eightbit_max_model/resnet50/conv4_block2_out/Relu__print__;__max:[22.913208]\n",
      ";model/resnet50/conv4_block2_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[22.913208]\n",
      ";model/resnet50/conv4_block3_2_conv/Conv2D_eightbit_min_model/resnet50/conv4_block3_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block3_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block3_2_conv/Conv2D_eightbit_max_model/resnet50/conv4_block3_1_relu/Relu__print__;__max:[18.8237762]\n",
      ";model/resnet50/conv4_block3_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[18.8237762]\n",
      ";model/resnet50/conv4_block3_3_conv/Conv2D_eightbit_min_model/resnet50/conv4_block3_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block3_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block3_3_conv/Conv2D_eightbit_max_model/resnet50/conv4_block3_2_relu/Relu__print__;__max:[31.9442768]\n",
      ";model/resnet50/conv4_block3_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[31.9442768]\n",
      ";model/resnet50/conv4_block4_1_conv/Conv2D_eightbit_min_model/resnet50/conv4_block3_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block3_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block4_1_conv/Conv2D_eightbit_max_model/resnet50/conv4_block3_out/Relu__print__;__max:[22.7079067]\n",
      ";model/resnet50/conv4_block3_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[22.7079067]\n",
      ";model/resnet50/conv4_block4_2_conv/Conv2D_eightbit_min_model/resnet50/conv4_block4_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block4_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block4_2_conv/Conv2D_eightbit_max_model/resnet50/conv4_block4_1_relu/Relu__print__;__max:[20.7787094]\n",
      ";model/resnet50/conv4_block4_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[20.7787094]\n",
      ";model/resnet50/conv4_block4_3_conv/Conv2D_eightbit_min_model/resnet50/conv4_block4_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block4_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block4_3_conv/Conv2D_eightbit_max_model/resnet50/conv4_block4_2_relu/Relu__print__;__max:[39.8405342]\n",
      ";model/resnet50/conv4_block4_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[39.8405342]\n",
      ";model/resnet50/conv4_block5_1_conv/Conv2D_eightbit_min_model/resnet50/conv4_block4_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block4_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block5_1_conv/Conv2D_eightbit_max_model/resnet50/conv4_block4_out/Relu__print__;__max:[21.9597511]\n",
      ";model/resnet50/conv4_block4_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[21.9597511]\n",
      ";model/resnet50/conv4_block5_2_conv/Conv2D_eightbit_min_model/resnet50/conv4_block5_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block5_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block5_2_conv/Conv2D_eightbit_max_model/resnet50/conv4_block5_1_relu/Relu__print__;__max:[18.5394783]\n",
      ";model/resnet50/conv4_block5_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[18.5394783]\n",
      ";model/resnet50/conv4_block5_3_conv/Conv2D_eightbit_min_model/resnet50/conv4_block5_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block5_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block5_3_conv/Conv2D_eightbit_max_model/resnet50/conv4_block5_2_relu/Relu__print__;__max:[32.0144958]\n",
      ";model/resnet50/conv4_block5_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[32.0144958]\n",
      ";model/resnet50/conv4_block6_1_conv/Conv2D_eightbit_min_model/resnet50/conv4_block5_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block5_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block6_1_conv/Conv2D_eightbit_max_model/resnet50/conv4_block5_out/Relu__print__;__max:[22.227663]\n",
      ";model/resnet50/conv4_block5_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[22.227663]\n",
      ";model/resnet50/conv4_block6_2_conv/Conv2D_eightbit_min_model/resnet50/conv4_block6_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block6_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block6_2_conv/Conv2D_eightbit_max_model/resnet50/conv4_block6_1_relu/Relu__print__;__max:[13.2899742]\n",
      ";model/resnet50/conv4_block6_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[13.2899742]\n",
      ";model/resnet50/conv4_block6_3_conv/Conv2D_eightbit_min_model/resnet50/conv4_block6_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block6_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv4_block6_3_conv/Conv2D_eightbit_max_model/resnet50/conv4_block6_2_relu/Relu__print__;__max:[24.1574841]\n",
      ";model/resnet50/conv4_block6_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[24.1574841]\n",
      ";model/resnet50/conv5_block1_1_conv/Conv2D_eightbit_min_model/resnet50/conv4_block6_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block1_0_conv/Conv2D_eightbit_min_model/resnet50/conv4_block6_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv4_block6_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block1_1_conv/Conv2D_eightbit_max_model/resnet50/conv4_block6_out/Relu__print__;__max:[26.4069519]\n",
      ";model/resnet50/conv5_block1_0_conv/Conv2D_eightbit_max_model/resnet50/conv4_block6_out/Relu__print__;__max:[26.4069519]\n",
      ";model/resnet50/conv4_block6_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[26.4069519]\n",
      ";model/resnet50/conv5_block1_2_conv/Conv2D_eightbit_min_model/resnet50/conv5_block1_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block1_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block1_2_conv/Conv2D_eightbit_max_model/resnet50/conv5_block1_1_relu/Relu__print__;__max:[9.74602509]\n",
      ";model/resnet50/conv5_block1_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[9.74602509]\n",
      ";model/resnet50/conv5_block1_3_conv/Conv2D_eightbit_min_model/resnet50/conv5_block1_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block1_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block1_3_conv/Conv2D_eightbit_max_model/resnet50/conv5_block1_2_relu/Relu__print__;__max:[6.3617959]\n",
      ";model/resnet50/conv5_block1_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[6.3617959]\n",
      ";model/resnet50/conv5_block1_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[-15.1536169]\n",
      ";model/resnet50/conv5_block1_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[18.1327171]\n",
      ";model/resnet50/conv5_block2_1_conv/Conv2D_eightbit_min_model/resnet50/conv5_block1_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block1_0_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block2_1_conv/Conv2D_eightbit_max_model/resnet50/conv5_block1_out/Relu__print__;__max:[38.0054588]\n",
      ";model/resnet50/conv5_block1_0_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[38.0054588]\n",
      ";model/resnet50/conv5_block2_2_conv/Conv2D_eightbit_min_model/resnet50/conv5_block2_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block2_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block2_2_conv/Conv2D_eightbit_max_model/resnet50/conv5_block2_1_relu/Relu__print__;__max:[6.66333437]\n",
      ";model/resnet50/conv5_block2_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[6.66333437]\n",
      ";model/resnet50/conv5_block2_3_conv/Conv2D_eightbit_min_model/resnet50/conv5_block2_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block2_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block2_3_conv/Conv2D_eightbit_max_model/resnet50/conv5_block2_2_relu/Relu__print__;__max:[6.86702156]\n",
      ";model/resnet50/conv5_block2_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[6.86702156]\n",
      ";model/resnet50/conv5_block3_1_conv/Conv2D_eightbit_min_model/resnet50/conv5_block2_out/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block2_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block3_1_conv/Conv2D_eightbit_max_model/resnet50/conv5_block2_out/Relu__print__;__max:[43.3868484]\n",
      ";model/resnet50/conv5_block2_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[43.3868484]\n",
      ";model/resnet50/conv5_block3_2_conv/Conv2D_eightbit_min_model/resnet50/conv5_block3_1_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block3_1_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block3_2_conv/Conv2D_eightbit_max_model/resnet50/conv5_block3_1_relu/Relu__print__;__max:[9.31518555]\n",
      ";model/resnet50/conv5_block3_1_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[9.31518555]\n",
      ";model/resnet50/conv5_block3_3_conv/Conv2D_eightbit_min_model/resnet50/conv5_block3_2_relu/Relu__print__;__min:[0]\n",
      ";model/resnet50/conv5_block3_2_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block3_3_conv/Conv2D_eightbit_max_model/resnet50/conv5_block3_2_relu/Relu__print__;__max:[11.1238222]\n",
      ";model/resnet50/conv5_block3_2_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[11.1238222]\n",
      ";model/resnet50/conv5_block3_3_conv/Conv2D_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/resnet50/conv5_block3_3_conv/Conv2D_eightbit_requant_range__print__;__requant_max:[69.0362]\n",
      ";model/resnet50/predictions/MatMul_eightbit_min_model/resnet50/avg_pool/Mean__print__;__min:[0]\n",
      ";model/resnet50/predictions/MatMul_eightbit_max_model/resnet50/avg_pool/Mean__print__;__max:[18.2891502]\n",
      ";model/resnet50/predictions/MatMul_eightbit_requant_range__print__;__requant_min:[-8.41893291]\n",
      ";model/resnet50/predictions/MatMul_eightbit_requant_range__print__;__requant_max:[32.3308601]\n",
      ";model/dense/MatMul_eightbit_min_model/resnet50/predictions/Softmax__print__;__min:[2.00718746e-18]\n",
      ";model/dense/MatMul_eightbit_max_model/resnet50/predictions/Softmax__print__;__max:[0.999996781]\n",
      ";model/dense_1/MatMul_eightbit_min_model/dense/Relu__print__;__min:[0]\n",
      ";model/dense/MatMul_eightbit_requant_range__print__;__requant_min:[0]\n",
      ";model/dense_1/MatMul_eightbit_max_model/dense/Relu__print__;__max:[0.296338916]\n",
      ";model/dense/MatMul_eightbit_requant_range__print__;__requant_max:[0.296338916]\n",
      ";model/dense_1/MatMul_eightbit_requant_range__print__;__requant_min:[-3.40207028]\n",
      ";model/dense_1/MatMul_eightbit_requant_range__print__;__requant_max:[5.7673831]\n",
      "2022-07-24 23:14:11 [INFO] Pass QuantizedRNNConverter elapsed time: 11.15 ms\n",
      "2022-07-24 23:14:11 [INFO] Pass StripUnusedNodesOptimizer elapsed time: 17.17 ms\n",
      "2022-07-24 23:14:11 [INFO] Pass RemoveTrainingNodesOptimizer elapsed time: 6.8 ms\n",
      "2022-07-24 23:14:11 [INFO] Pass FoldBatchNormNodesOptimizer elapsed time: 6.11 ms\n",
      "2022-07-24 23:14:11 [INFO] Pass MetaOpOptimizer elapsed time: 3.43 ms\n",
      "2022-07-24 23:14:12 [INFO] Pass PostCseOptimizer elapsed time: 402.7 ms\n",
      "2022-07-24 23:14:12 [INFO] |******Mixed Precision Statistics*****|\n",
      "2022-07-24 23:14:12 [INFO] +-----------------+----------+--------+\n",
      "2022-07-24 23:14:12 [INFO] |     Op Type     |  Total   |  INT8  |\n",
      "2022-07-24 23:14:12 [INFO] +-----------------+----------+--------+\n",
      "2022-07-24 23:14:12 [INFO] |      Conv2D     |    53    |   53   |\n",
      "2022-07-24 23:14:12 [INFO] |      MatMul     |    3     |   3    |\n",
      "2022-07-24 23:14:12 [INFO] |     MaxPool     |    1     |   1    |\n",
      "2022-07-24 23:14:12 [INFO] |    QuantizeV2   |    4     |   4    |\n",
      "2022-07-24 23:14:12 [INFO] |    Dequantize   |    4     |   4    |\n",
      "2022-07-24 23:14:12 [INFO] +-----------------+----------+--------+\n",
      "2022-07-24 23:14:12 [INFO] Pass quantize model elapsed time: 20131.26 ms\n",
      "2022-07-24 23:14:12 [INFO] Start to evaluate the TensorFlow model.\n",
      "2022-07-24 23:14:24 [INFO] Model inference elapsed time: 11801.46 ms\n",
      "2022-07-24 23:14:24 [INFO] Tune 1 result is: [Accuracy (int8|fp32): 0.9789|0.9794, Duration (seconds) (int8|fp32): 11.8027|27.3783], Best tune result is: [Accuracy: 0.9789, Duration (seconds): 11.8027]\n",
      "2022-07-24 23:14:24 [INFO] |**********************Tune Result Statistics**********************|\n",
      "2022-07-24 23:14:24 [INFO] +--------------------+----------+---------------+------------------+\n",
      "2022-07-24 23:14:24 [INFO] |     Info Type      | Baseline | Tune 1 result | Best tune result |\n",
      "2022-07-24 23:14:24 [INFO] +--------------------+----------+---------------+------------------+\n",
      "2022-07-24 23:14:24 [INFO] |      Accuracy      | 0.9794   |    0.9789     |     0.9789       |\n",
      "2022-07-24 23:14:24 [INFO] | Duration (seconds) | 27.3783  |    11.8027    |     11.8027      |\n",
      "2022-07-24 23:14:24 [INFO] +--------------------+----------+---------------+------------------+\n",
      "2022-07-24 23:14:24 [INFO] Save tuning history to /home/projects/BigDL/python/nano/notebooks/tensorflow/tutorial/nc_workspace/2022-07-24_23-13-08/./history.snapshot.\n",
      "2022-07-24 23:14:24 [INFO] Specified timeout or max trials is reached! Found a quantized model which meet accuracy goal. Exit.\n",
      "2022-07-24 23:14:24 [INFO] Save deploy yaml to /home/projects/BigDL/python/nano/notebooks/tensorflow/tutorial/nc_workspace/2022-07-24_23-13-08/deploy.yaml\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.metrics import CategoricalAccuracy\n",
    "q_model = model.quantize(x=calib_ds,\n",
    "                         metric=CategoricalAccuracy(),\n",
    "                         tuning_strategy='basic'\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "\n",
    "# run simple prediction\n",
    "m1 = CategoricalAccuracy()\n",
    "start = time()\n",
    "for img, label in test_ds:\n",
    "    m1.update_state(label, q_model(img))\n",
    "infer_time_int8 = time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "|    Precision    |    Infer Time(s)  | Accuracy(%) |\n",
      "|      fp32       |       21.37       |    97.94    |\n",
      "|      int8       |        5.21       |    97.83    |\n",
      "| Improvement(%)  |       75.63       |    -0.10    |\n",
      "\n"
     ]
    }
   ],
   "source": [
    "template = \"\"\"\n",
    "|    Precision    |    Infer Time(s)  | Accuracy(%) |\n",
    "|      fp32       |       {:5.2f}       |    {:5.2f}    |\n",
    "|      int8       |       {:5.2f}       |    {:5.2f}    |\n",
    "| Improvement(%)  |       {:5.2f}       |    {:5.2f}    |\n",
    "\"\"\"\n",
    "summary = template.format(\n",
    "    fp32_infer_time, his[1] * 100,\n",
    "    infer_time_int8, m1.result().numpy() * 100,\n",
    "    (1 - infer_time_int8/fp32_infer_time) * 100,  (m1.result().numpy()/his[1] - 1) * 100\n",
    ")\n",
    "print(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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